Determining Investment Opportunities and Directions for Artificial Intelligence and Machine Learning Applied to Digital Twin Technology in Semiconductor Manufacturing

The National Institute for Standards and Technology (NIST) established a United States (US)-based industrial Artificial Intelligence (AI) consortium to address the high-priority technical barriers in semiconductor manufacturing high-mix production and provide a roadmap for US manufacturers to improve overall competitiveness in a global marketplace. As part of this process, challenge categories were defined, with a key challenge category being "Digital Twin" (DT). In investigating this challenge category, it was determined that DTs are already being successfully leveraged in the industry for solutions such as predictive maintenance and model-based process control, however significant opportunities exist if investment is focused on key AI and Machine Learning (ML) areas. This document is a position paper presenting challenges, potential solutions and a roadmap for advancing DTs and associated AI in the manufacturing space. A key conclusion is the need for technical DT and DT framework standards, including a taxonomy and definition of interfaces. With these standards in place, opportunities exist for improvement of existing DT-driven capabilities, as well as realization of new capabilities, especially those resulting from the interoperability DT classes throughout the manufacturing ecosystem.